R Tutorial : Experimental Design in R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/experimental-design-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, my name is Kaelen Medeiros and I’m a data scientist who works in the technology and health industries. I’m here to teach you about experimental design in R. An experiment starts with a question. The experiment involves collecting data with the question in mind and will include analyzing the data to seek an answer. In this course, we’ll focus on asking good questions - in statistical language, formulating clear hypotheses, design the data collection process, and the analysis of collected data. The three high-level steps of an experiment are planning, design, and analysis. For planning, you start with your hypothesis -- your question, or even a series of questions. What are you hoping to answer? What is the population of interest, those to whom it applies? What will your dependent variable be, the outcome, which hopefully can be measured to answer the question? What are your independent or explanatory variables, the variables you think may explain the dependent variable? Design questions may naturally follow from planning questions. Choosing a design might entail knowing you want to study different variables and the possible interaction effects of those variables, so you choose a factorial design. Then, if your dependent variable will be a yes/no answer, you know you’re going to be dealing with some kind of logistic regression when you get to analysis. We’re using open data in this course, so we don't know the original experimental design, but that’s okay. The data we'll use throughout the course has been cleaned and altered by me as if it was collected as part of our experiments. Here's an example timeline for an experiment I designed at a past job. There's no standard for timelines, however! The three key components of an experiment include